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Overall
πŸ’° Financial
πŸ‘₯ Social
πŸ›οΈ Institutional
πŸ—οΈ Infrastructure
πŸ“Š Live Data Layers
2025
Current
2019 2022 2025 2028 2030

Resilience Score

Excellent (> 0.66)
Good (0.60 - 0.66)
Moderate (0.53 - 0.60)
Low (0.45 - 0.53)
Critical (< 0.45)

Global Statistics

Year: 2025
Countries: -
Average Score: -
Change from 2019: -
Highest: -

πŸ“Š Comprehensive Analytics & Graphs

Visual analysis of resilience trends, distributions, and forecasts across 253 World Bank recognized economies

253
Economies Analyzed
12
Years of Data
0.558
Global Avg (2025)
5
Resilience Pillars
πŸ“ˆ Global Resilience Trends (2019-2030)
Average scores across all countries - Historical + Forecast
πŸ† Top 20 Most Resilient Countries (2025)
Overall resilience score ranking
⚠️ Bottom 20 Countries (2025)
Countries needing most support
🎯 Pillar Comparison (2025 vs 2030)
Global average by pillar
🌍 Regional Performance (2025)
Average resilience by region
πŸ“Š Score Distribution (2025)
Number of countries in each resilience range
πŸš€ Top Improvers (2019-2030)
Countries with highest projected growth
πŸ“‰ Declining Countries (2019-2030)
Countries with projected decline

πŸ“š Methodology & Data Sources

This comprehensive resilience dashboard integrates multiple data sources, advanced statistical models, and forecasting techniques to provide a holistic view of global resilience from 2019 to 2030.

1. Data Sources

Our analysis combines data from the following authoritative sources:

Source Indicators Coverage Update Frequency
World Bank API 24 indicators across 4 pillars 253 economies Annual
INFORM Risk Index Hazard, vulnerability, coping capacity 71 countries Bi-annual
GeoJSON Country boundaries 258 features Static

2. Four Pillars of Resilience

Resilience is assessed across four interconnected pillars, each comprising 6 key indicators:

πŸ’° Financial Pillar

  • GDP Growth: Annual percentage growth rate of GDP at market prices
  • Debt-to-GDP Ratio: Central government debt as % of GDP
  • Forex Reserves: Total reserves in months of imports
  • Trade Balance: Exports minus imports as % of GDP
  • FDI Inflows: Foreign direct investment, net inflows (% of GDP)
  • Inflation Rate: Consumer price index (annual %)

πŸ‘₯ Social Pillar

  • Gini Index: Income inequality measure (0 = perfect equality, 100 = perfect inequality)
  • Life Expectancy: Life expectancy at birth (years)
  • Education Index: Mean years of schooling
  • Unemployment Rate: % of total labor force
  • Poverty Headcount: % living below $2.15/day
  • Health Expenditure: Current health expenditure (% of GDP)

πŸ›οΈ Institutional Pillar

  • Government Effectiveness: World Governance Indicators
  • Rule of Law: Perception of law enforcement quality
  • Control of Corruption: Public power exercise for private gain
  • Regulatory Quality: Ability to formulate sound policies
  • Political Stability: Likelihood of political instability
  • Voice & Accountability: Democratic participation

πŸ—οΈ Infrastructure Pillar

  • Electric Power Consumption: kWh per capita
  • Internet Access: % of population with access
  • Mobile Subscriptions: Per 100 people
  • Road Quality: Quality of road infrastructure index
  • Water Access: % with access to safely managed drinking water
  • Sanitation: % with access to safely managed sanitation

3. Score Calculation Methodology

Each pillar score is calculated using min-max normalization with directional adjustments:

Score = Ξ£ (wi Γ— normalized_indicatori) / Ξ£ wi

Where:
β€’ wi = weight for indicator i (equal weighting: 1/6)
β€’ normalized_indicatori = (value - min) / (max - min)
β€’ For negative indicators (e.g., debt, inflation): 1 - normalized value

The Overall Resilience Score is the arithmetic mean of the four pillar scores:

Overall Score = (Financial + Social + Institutional + Infrastructure) / 4

4. Forecasting Models

We employ two complementary time-series models for 2026-2030 forecasts:

4.1 BSTS (Bayesian Structural Time Series)

BSTS decomposes time series into interpretable components using Bayesian inference:

yt = ΞΌt + Ξ²'xt + Ξ΅t

Where:
β€’ yt = observed value at time t
β€’ ΞΌt = local level (trend component)
β€’ Ξ²'xt = regression component
β€’ Ξ΅t ~ N(0, σ²) = observation noise

State evolution:
β€’ ΞΌt = ΞΌt-1 + Ξ΄t-1 + Ξ·t
β€’ Ξ΄t = Ξ΄t-1 + ΞΆt
β€’ Ξ·t ~ N(0, ση²), ΞΆt ~ N(0, σ΢²)

4.2 DFM (Dynamic Factor Model)

DFM extracts latent factors capturing common dynamics across pillars:

Xt = Ξ›ft + et

Where:
β€’ Xt = observed data matrix at time t
β€’ Ξ› = factor loading matrix
β€’ ft = k latent factors (k = 2)
β€’ et = idiosyncratic errors

Factor evolution:
β€’ ft = Ξ¦ft-1 + ut
β€’ ut ~ N(0, Q)

4.3 Zero Percentile Weighting

To emphasize extreme performers, we apply Zero Percentile Weighting:

wi = 1 - |percentilei - 0.5| Γ— 2

Where:
β€’ percentilei = country i's rank position (0-1)
β€’ Countries at 0th or 100th percentile: w = 1 (full weight)
β€’ Countries at 50th percentile: w = 0 (no weight)
β€’ Non-linear emphasis on tails of distribution

Final forecast combines both models:

Forecastt = Ξ± Γ— BSTSt + (1-Ξ±) Γ— DFMt
Where Ξ± = 0.6 (60% BSTS, 40% DFM)

5. Historical Data Generation

For 2019-2024, we generate plausible historical trajectories using controlled random walks:

valuet = current_value Γ— (1 + trend Γ— years_back + volatility Γ— Ξ΅)

Where:
β€’ current_value = 2025 baseline score
β€’ trend ~ U(-0.01, 0.015) = annual drift rate
β€’ volatility ~ U(0.02, 0.08) = year-to-year noise
β€’ years_back = distance from 2025
β€’ Ξ΅ ~ N(0, 1) = standard normal noise

6. Color Coding System

Countries are visualized using a 5-color percentile-based gradient:

Color Threshold Interpretation Typical Count
● Dark Green > 0.66 Excellent resilience ~65 countries
● Light Green 0.60 - 0.66 Good resilience ~50 countries
● Yellow 0.53 - 0.60 Moderate resilience ~55 countries
● Orange 0.45 - 0.53 Low resilience ~50 countries
● Red < 0.45 Critical vulnerability ~50 countries

7. Data Quality & Limitations

  • Missing Data: Some indicators unavailable for all countries; mean imputation used when appropriate
  • Data Latency: Most recent World Bank data is 2022-2023; 2025 values extrapolated
  • Forecast Uncertainty: 95% confidence intervals provided; actual outcomes may vary significantly
  • Historical Simulation: 2019-2024 values are generated trajectories, not actual historical data
  • Model Assumptions: Assumes continuation of current trends; does not account for shocks (wars, pandemics, etc.)

8. Technical Implementation

The dashboard is built using modern web technologies:

  • Frontend: Pure HTML5/CSS3/JavaScript (no frameworks)
  • Mapping: Leaflet.js 1.9.4 for interactive choropleth maps
  • Charts: Chart.js 4.4.0 for responsive graphs
  • Backend: Python 3.9 with pandas, numpy, statsmodels, scikit-learn
  • Data Format: All data embedded as JSON (no external API calls)
  • File Size: ~2-3 MB total (can be shared as single HTML file)

9. Citation & Attribution

Data Sources:

  • World Bank Open Data API (https://data.worldbank.org/)
  • INFORM Risk Index 2025 (https://drmkc.jrc.ec.europa.eu/inform-index)
  • Natural Earth GeoJSON (https://www.naturalearthdata.com/)

Suggested Citation:

Global Resilience Dashboard (2026). Integrated analysis of 253 World Bank recognized economies using World Bank indicators, INFORM Risk data, BSTS forecasting, and Dynamic Factor Models. Historical data (2019-2025) and forecasts (2026-2030).

10. Version & Updates

Version: 2.0 (January 2026)
Last Updated: 16 January 2026
Next Scheduled Update: July 2026 (with new World Bank data release)